Data-driven traffic engineering: techniques, experiences and challenges

Abstract

This paper presents a global view of measurement-driven traffic engineering, explores the interplay between traffic matrix estimation and routing optimization and demonstrates how demand uncertainties can be accounted for in the optimization step to guarantee a robust and reliable result. Based on a unique data set of complete measured traffic matrices, we quantify the demand uncertainties in an operational IP network and demonstrate how a number of robust optimization schemes allow to find fixed MPLS configurations that are close to the performance limits given by time-varying routing under full demand knowledge. We present a novel scheme for computing a sparse MPLS mesh to complement a baseline routing, and explore how the performance depends on the size of the partial mesh. Corresponding methods for robust OSPF optimization are discussed and a number of challenges are detailed.